Three Questions for Reza Sadri, Director of the AI Bootcamp

Credit: Vicki Chiu/Caltech.

Reza Sadri, the director of the new AI Bootcamp in the Division of Engineering and Applied Science, joined Caltech in 2023, bringing with him 30 years of experience in software development, including as head of machine learning infrastructure at Instacart. He hopes to bridge the gap between academia and AI technology through a series of eight to 10 workshops a year aimed at Caltech graduate students, postdocs, and researchers. Here, Sadri provides some more details about that effort.

What is the AI Bootcamp?

The main goal of the bootcamp is to help participants understand when and how to effectively use AI within their research as well as to identify its appropriate contexts and limitations. Forty years ago, there were a lot of physicists and chemists who could benefit from using a computer, but they didn’t know how. AI is at the stage that the computer was 40 years ago. We must bring scientists on board to be able to use AI effectively.

What are some ways AI is used ineffectively in industrial and research settings?

First, sometimes, you don’t need AI. There are some applications where you can get by using simple mathematical models or statistics. The second mistake is sometimes people use a complicated model when a simple model will work just as well. The third is mishandling data. Effective use of data requires clean, relevant, and non-leaky datasets—data leakage is when information from outside the training dataset is inadvertently included in the model, leading to unrealistic performance. Misusing data like this leads to wasted efforts such as publishing papers based on incorrect data assumptions.

What is the long-term vision for the bootcamp?

In the past decade, AI has expanded significantly into various branches and applications, and some AI applications are well suited to specific fields or problems. We will offer specialized bootcamps for these applications, such as reinforcement learning, graph neural networks, and large-scale data processing. The broader application of AI across diverse scientific disciplines inherently enriches AI and machine learning. Scientific research often tackles unconventional problems that are not mainstream, presenting unique challenges. Addressing these issues with AI and machine learning necessitates innovative approaches, pushing the boundaries of the field in unexpected directions.